System and Method for Root Cause Analysis and Early Warning of Inventory Problems

ABSTRACT

A system and method is disclosed for root cause analysis and early warning of inventory problems. The system includes a server coupled with a database and configured to access the data describing inventory policy parameters of a supply chain network, the data describing one or more demand patterns and one or more replenishment patterns of the supply chain network, and the data describing the supply chain network comprising a plurality of entities, each entity configured to supply one or more items to satisfy a demand. The server is further configured to optimize the inventory policy parameters for each of the one or more items according to the one or more demand patterns and the one or more replenishment patterns and store the optimized inventory policy parameters in the database for each of the one or more items.

CROSS-REFERENCE TO RELATED APPLICATIONS

The present invention is related to that disclosed in U.S. ProvisionalPatent Application Ser. No. 61/116,287, filed 19 Nov. 2008, entitled “AFramework for Inventory Performance Root Cause Analysis.” U.S.Provisional Patent Application Ser. No. 61/116,287 is assigned to theassignee of the present application. The subject matter disclosed inU.S. Provisional Patent Application Ser. No. 61/116,287 is herebyincorporated by reference into the present disclosure as if fully setforth herein. The present invention hereby claims priority under 35U.S.C. §119(e) to U.S. Provisional Patent Application Ser. No.61/116,287.

TECHNICAL FIELD OF THE INVENTION

This invention relates generally to inventory planning, and moreparticularly to system and method for root cause analysis and earlywarning of inventory problems.

BACKGROUND OF THE INVENTION

Due to global supply and distribution networks, finished goods (FG) andcomponents travel around the globe as they are procured manufactured anddistributed to reach the end customer or consumer. Managing finishedgoods and component inventory in conditions where demand pattern variesacross markets, product lifecycles evolve and sale prices erode overtime is a challenging and daunting task. If, for example, components areprocured too early and not consumed, the buying entity ends up losingmoney on price leverage on the one hand and taking aging inventorylosses on the other. Conversely if inventory shortages occur due toinsufficient supply, then revenue opportunities are missed. Theinability to effectively manage finished goods and component inventoryis undesirable.

SUMMARY OF THE INVENTION

A system for root cause analysis and early warning of inventory problemsis disclosed. The system includes a database that stores data describinginventory policy parameters of a supply chain network, data describingone or more demand patterns and one or more replenishment patterns ofthe supply chain network, and data describing the supply chain networkcomprising a plurality of entities, each entity configured to supply oneor more items to satisfy a demand. The system further includes a servercoupled with a database and configured to access the data describinginventory policy parameters of a supply chain network, the datadescribing one or more demand patterns and one or more replenishmentpatterns of the supply chain network, and the data describing the supplychain network comprising a plurality of entities, each entity configuredto supply one or more items to satisfy a demand. The server is furtherconfigured to optimize the inventory policy parameters for each of theone or more items according to the one or more demand patterns and theone or more replenishment patterns and store the optimized inventorypolicy parameters in the database for each of the one or more items.

A method of for root cause analysis and early warning of inventoryproblems is also disclosed. The method provides for accessing, by acomputer, data describing the inventory policy parameters, datadescribing the one or more demand patterns and the data describing theone or more replenishment patterns, and data describing the supply chainnetwork. The method further provides for optimizing, by the computer,the inventory policy parameters for each of the one or more itemsaccording to the one or more demand patterns and the one or morereplenishment patterns and storing, by the computer, the optimizedinventory policy parameters in the database for each of the one or moreitems.

A computer-readable medium embodied with software enabling root causeanalysis and early warning of inventory problems in a supply chainnetwork is also disclosed. The software when executed using one or morecomputers is configured to access data describing the inventory policyparameters, data describing the one or more demand patterns and the datadescribing the one or more replenishment pattern, and data describingthe supply chain network. The software is further configured to optimizethe inventory policy parameters for each of the one or more itemsaccording to the one or more demand patterns and the one or morereplenishment patterns and store the optimized inventory policyparameters in the database for each of the one or more items.

BRIEF DESCRIPTION OF THE DRAWINGS

The novel features believed characteristic of the invention are setforth in the appended claims. However, the invention itself, as well asa preferred mode of use, and further objectives and advantages thereof,will best be understood by reference to the following detaileddescription when read in conjunction with the accompanying drawings,wherein:

FIG. 1 illustrates an exemplary system according to a preferredembodiment;

FIG. 2 illustrates an exemplary method of a planning process fordetermining root causes of inventory performance issues;

FIG. 3 illustrates an exemplary method of an execution process fordetermining root causes of inventory performance issues; and

FIG. 4 illustrates an exemplary method of analyzing the deviation ofactual demand against forecasted demand and providing alert rules.

DETAILED DESCRIPTION OF THE INVENTION

Reference will now be made to the following detailed description of thepreferred and alternate embodiments. Those skilled in the art willrecognize that the present invention provides many inventive conceptsand novel features, that are merely illustrative, and are not to beconstrued as restrictive. Accordingly, the specific embodimentsdiscussed herein are given by way of example and do not limit the scopeof the present invention.

FIG. 1 illustrates exemplary system 100 according to a preferredembodiment. System 100 comprises supply chain planner 110, one or moresupply chain entities 120 a-120 n, a network 130, and communicationlinks 132 and 134 a-134 n. Although a single supply chain planner 110,one or more supply chain entities 120 a-120 n, and a single network 130,are shown and described; embodiments contemplate any number of supplychain planners 110, any number of supply chain entities 120 a-120 n,and/or any number of networks 130, according to particular needs. Inaddition, or as an alternative, supply chain planner 110 may be integralto or separate from the hardware and/or software of any one of the oneor more supply chain entities 120 a-120 n.

In one embodiment, one or more supply chain entities 120 a-120 nrepresent one or more supply chain networks including one or moreentities, such as, for example suppliers, manufacturers, distributioncenters, retailers, and/or customers. A supplier may be any suitableentity that offers to sell or otherwise provides one or more items(i.e., materials, components, or products) to one or more manufacturers.A manufacturer may be any suitable entity that manufactures at least onefinished good. A manufacturer may use one or more items during themanufacturing process to produce a finished good. In this document, thephrase “finished good” may refer to any manufactured, fabricated,assembled, or otherwise processed item, material, component, good orproduct. A finished good may represent an item ready to be supplied to,for example, another supply chain entity in system 100, such as asupplier, an item that needs further processing, or any other item. Amanufacturer may, for example, produce and sell a finished good to asupplier, another manufacturer, a distribution center, a retailer, acustomer, or any other suitable person or entity. A distribution centermay be any suitable entity that offers to sell or otherwise distributesat least one finished good to one or more retailers and/or customers. Aretailer may be any suitable entity that obtains one or more finishedgoods to sell to one or more customers.

Although one or more supply chain entities 120 a-120 n are shown anddescribed as separate and distinct entities, the same person or entitycan simultaneously act as any one of the one or more supply chainentities 120 a-120 n. For example, one or more supply chain entities 120a-120 n acting as a manufacturer could produce a finished good, and thesame entity could act as a supplier to supply an item to another supplychain. Although one example of a supply chain network is shown anddescribed, embodiments contemplate any operational environment and/orsupply chain network, without departing from the scope of the presentinvention.

Supply chain planner 110 comprises one or more computers 112, server114, and databases 116. Server 114 comprises one or more planningengines 114 a. Although server 114 is shown and described as comprisingone or more planning engines 114 a, embodiments contemplate any suitableengines, solvers, or combination of engines and/or solvers, according toparticular needs.

Database 116 comprises one or more databases or other data storagearrangements at one or more locations, local to, or remote from, server114. Databases 116 may be coupled with server 114 using one or morelocal area networks (LANs), metropolitan area networks (MANs), wide areanetworks (WANs), network 130, such as, for example, the Internet, or anyother appropriate wire line, wireless, or other links. Databases 116stores data that describes one or more supply chain networks of one ormore supply chain entities 120 a-120 n and describes one or more supplychain entities 120 a-120 n. Databases 116 may include data representing,for example, demand patterns 116 a, replenishment patterns 116 b,business rules 116 c, inventory policy parameters 116 d, and inventorydata 116 e.

Business rules 116 c encodes specific business rules associated with oneor more supply chain entities 120 a-120 n to identify known patterns ofbehavior. For example, business rules 116 c provides for each rule todetect a particular failure mode which may operate on its own,independent of what other failure modes may already exist. In addition,or as an alternative, each rule may trigger independently of other rulesand the same supply chain event within a supply chain network of one ormore supply chain entities 120 a-120 n may trigger multiple rules.Supply chain planner 110 resolves when multiple rules are triggered atthe same time for the same event. In one embodiment, a strict precedenceis used between rules to allow only one rule to dominate. In anotherembodiment, overlaps between rules may be resolved with yet additionalinference rules stored in business rules 116 c, that is, when supplychain planner 110 identifies more than one root cause for the sameevent.

In one embodiment, one or more planning engines 114 a comprise aninference engine which operates systematically over inventory data 116e, such as, for example, performance history data, forecast historydata, supplier collaboration history data, replenishment history dataand inventory targets history data. The inference engine triggersbusiness rules 116 c to resolve overlaps or conflicts between businessrules 116 c. In addition, or as an alternative, the inference enginedetermines if the final root cause is independent of the individualrules that identify individual suspicions. In one embodiment, theinference engine provides for an implementation with a database query ofdatabase 116. In another embodiment, the inference engine is implementedseparate from business rules 116 c, that is the inference engine isimplemented on rules without knowing what the rule is.

In one embodiment, supply chain planner 110 and/or one or more supplychain entities 120 a-120 n may each operate on one or more computersystems that are integral to or separate from the hardware and/orsoftware that support system 100. These one or more computer systems mayinclude any suitable input device, such as a keypad, mouse, touchscreen, microphone, or other device to input information. An outputdevice may convey information associated with the operation of supplychain planner 110 and one or more supply chain entities 120 a-120 n,including digital or analog data, visual information, or audioinformation. These one or more computer systems may include fixed orremovable computer-implemented storage media, such as magnetic computerdisks, CD-ROM, or other suitable media to receive output from andprovide input to system 100. These one or more computer systems mayinclude one or more processors and associated memory to executeinstructions and manipulate information according to the operation ofsystem 100.

Supply chain planner 110 and one or more supply chain entities 120 a-120n may each operate on separate computer systems or may operate on one ormore shared computer systems. Each of these one or more computer systemsmay be a work station, personal computer (PC), network computer,notebook computer, personal digital assistant (PDA), cell phone,telephone, wireless data port, or any other suitable computing device.

In an embodiment, one or more users may be associated with supply chainplanner 110 and/or one or more supply chain entities 120 a-120 n. Theseone or more users may include, for example, an “analyst” handlingmanagement and planning of the supply chain and/or one or more relatedtasks within system 100. In addition, or as an alternative, these one ormore analysts within system 100 may include, for example, one or morecomputer systems programmed to autonomously handle planning and/or oneor more related tasks within system 100. As discussed above, server 114may support one or more planning engines 114 a, including one or moresupply chain replenishment planning engines, to generate supply chainplans based on inputs accessed or received from one or more analysts,one or more demand patterns 116 a, replenishment patterns 116 b,business rules 116 c, inventory policy parameters 116 d, and inventorydata 116 e, as described more fully below.

In one embodiment, supply chain planner 110 is coupled with network 130using communications link 132, which may be any wireline, wireless, orother link suitable to support data communications between supply chainplanner 110 and network 130 during operation of system 100. One or moresupply chain entities 120 a-120 n are coupled with network 130 usingcommunications links 134 a-134 n, which may be any wireline, wireless,or other link suitable to support data communications between one ormore supply chain entities 120 a-120 n and network 130 during operationof system 100. Although communication links 132 and 134 a-134 n areshown as generally coupling supply chain planner 110 and one or moresupply chain entities 120 a-120 n to network 130, supply chain planner110 and one or more supply chain entities 120 a-120 n may communicatedirectly with each other, according to particular needs.

In addition, or as an alternative, network 130 may include the Internetand any appropriate local area networks (LANs), metropolitan areanetworks (MANS), or wide area networks (WANs) coupling supply chainplanner 110 and one or more supply chain entities 120 a-120 n. Forexample, data may be maintained by supply chain planner 110 at one ormore locations external to supply chain planner 110 and one or moresupply chain entities 120 a-120 n and made available to one or moreassociated users of one or more supply chain entities 120 a-120 n usingnetwork 130 or in any other appropriate manner. Those skilled in the artwill recognize that the complete structure and operation ofcommunication network 130 and other components within system 100 are notdepicted or described. Embodiments may be employed in conjunction withknown communications networks and other components.

In one embodiment and as discussed in more detail below, system 100provides an automated analysis of large amounts of execution performancedata to identify patterns that point to specific root causes. That is,system 100 comprises key inputs that include demand forecast waterfallhistory and future forecast, shipment/sales history, inventory history,receipts history and forecast (Advance Ship Notices/Commits), inventorytargets history and forecast, plus a library of parameterized businessrules. In addition, system 100 comprises key outputs that includeautomated alerts, for example, where demand forecast is out-of-synchwith actual, where replenishments are out of synch with forecasts andactual, and where surprise large orders may have occurred. Accordingly,system 100 rapidly diagnosis, for example, if an inventory problem was aresult of demand process issues and/or supply process issues. Inaddition, as explained in more detail in FIGS. 2-4, system 100 providesfor a Plan-Do-Check-Act process that facilitates an auto-learning andself-tuning system.

FIG. 2 illustrates an exemplary method 200 of a planning process fordetermining root causes of inventory performance issues. Supply chainplanner 110 begins the method at step 202 by analyzing demand patterns116 a of database 116. Demand patterns 116 a include, for example, datarepresenting previous forecast demand (historical forecast demand) andactual demand associated with one or more supply chain networks ofsupply chain entities 120 a-120 n. Supply chain planner 110 analyzesdemand patterns 116 a using statistical techniques to compare theforecasted demand with the actual demand and quantify the deviationusing, for example, Weighted Mean Absolute Percent Error. Although aparticular statistical technique is described, any suitable statisticaltechnique or combination of statistical techniques may be applied tocompare the forecasted demand with the actual demand.

In one embodiment, supply chain planner 110 analyzes demand patterns 116a and quantifies the deviation (i.e., quantification of risk), toaccount for lumpiness, data hygiene factor, and the aggregation levelacross product geography and time (i.e., appropriate selection of levelof abstraction across product, geography and time). Lumpiness representshow clumped or large the demand is relative to its frequency, that is,for example, the distribution of demand over time and how often and howbig the demand is. For example, if one or more supply chain entities 120a-120 n sells items one per day at a steady rate, the demand is about 30items per month and there is no lumpiness. However, if one or moresupply chain entities 120 a-120 n sells the same 30 items a month butsells all 30 items in one lump in, for example, the middle of the month,the monthly forecast of 30 items per month is the same, however, thedemand pattern in the latter is lumpy, where the demand pattern in theformer is not lumpy. Data Hygiene factor is selectively qualifyingand/or repairing data based on one or more business rules 116 c storedin database 116.

In addition, or as an alternative, if any of demand patterns 116 a datais unclean, supply chain planner 110 filters or corrects the data. Forexample, the cleanliness of demand patterns 116 a data may be such thatunclean data is presented to system 100, because, for example, the dataof one or more supply chain entities 120 a-120 n may not have been wellmaintained. Supply chain planner 110 analyzes demand patterns 116 a datafor patterns of bad data and detects and repairs (i.e., either filtersor corrects) the bad data as necessary.

At step 204, supply chain planner 110 analyzes supply replenishmentpatterns 116 b stored in database 116 (i.e., supply side ofreplenishment patterns) of one or more supply chain networks of supplychain entities 120 a-120 n and quantifies the supply risk utilizing, forexample, lumpiness and the hygiene factor. Replenishment patterns 116 binclude, for example, data representing the size and frequency ofreplenishments of one or more supply chain networks of supply chainentities 120 a-120 n.

At step 206, supply chain planner 110 optimizes inventory policyparameters 116 d with up-to-date quantified demand and/or supply risk,including any business objectives stored in business rules of database116. In addition, supply chain planner 110 analyzes and determinesassumptions that go into the optimization and detects root causes inorder to fine tune the performance of inventory policy parameters 116 d.

At step 208, supply chain planner 110 periodically compares inventorypolicy parameters 116 d with selective updates. That is, supply chainplanner 110 selectively updates inventory policy parameters 116 d on anas-needed basis, such that, all inventory policy parameters are reviewedbut not necessarily updated on fixed intervals, such as for example,every week. In addition, the periodic comparison may be provided on anyscheduled time, such as, for example, daily, weekly, monthly, or anyother periodic time. However, the periodic comparison is selectivelyprovided to decide which inventory policy parameters 116 d to updatebased on, for example, the above steps 202-206.

At step 210, supply chain planner 110 publishes updated inventory policyparameters 116 d from step 208 to one or more planning engines 114 a,for example, supply chain replenishment planning engines, of one or moresupply chain entities 120 a-120 n. For example, the published inventorypolicy parameters 116 d are input into the one or more supply chainreplenishment planning engines to generate an executable supply chainplan. That is, executable supply chain plans are generated and include,for example, what to make, when to make it, what to move, when to moveit, and the like. As discussed above, supply chain planner 110 may beintegral to or separate from the hardware and/or software of any one ofthe one or more supply chain entities 120 a-120 n and associated withone or more supply chain networks. In addition, supply chain planner 110may be operated on a global level, that is, supply chain planner 110 mayspan the whole make-move-store plan (i.e., master planning).

At step 212, supply chain planner 110 executes inventory replenishmentsand movements as planned in step 210. In addition, or as an alternative,supply chain planner 110 provides for inventory replenishments andmovements that may be planned in order to meet demand and to maintaininventory levels within one or more inventory policy parameters 116 d.

FIG. 3 illustrates an exemplary method 300 of an execution process fordetermining root causes of inventory performance issues. Supply chainplanner 110 begins the method at step 302 by checking inventory levelsof inventory data 116 e across time and determining how the inventorylevels performed against published target levels. In one embodiment,this includes both a backward retrospective looking at past inventoryperformance as well as a forward looking early warning analysis forimpending performance issues. In addition, or as an alternative, endinginventory levels for each item at each location (i.e., entity orlocation within an entity) in each period is coded according to acomparison to published targets. Supply chain planner 110 assigns acoding value to each item. In another embodiment, supply chain planner110 provides for the coding value to be managed via user configurablethresholds against targets in terms of quantity and/or days of coverage.Exemplary coding values may be as follows:

TABLE 1 Code Value Inventory Level Code 1 - Red Inventory stocked out ordangerously below target Code 2 - Yellow Inventory too low and at highrisk of stock-out Code 3 - Green Inventory is just right and withinacceptable tolerances Code 4 - Blue Inventory is too high Code 5 -Purple Inventory is excessively too high and may be at risk ofobsolescence

Although an example coding is illustrated and described herein,embodiments contemplate any suitable coding and/or any other suitabletechnique for providing a framework to articulate rules over the endinginventory levels.

At step 304, supply chain planner 110 analyzes actual demand againstpredicted demand as well as the evolution of the prediction over time(i.e., a forecast waterfall). That is, supply chain planner 110determines what was the actual demand against what was the predictionand how did that prediction evolve over time. As an example only, andnot by way of limitation, supply chain planner 110 determines atimeframe of when a first indication occurred as to when things weregoing to be different than predicted and uses that to anticipate suchevents in the future.

In one embodiment, supply chain planner 110 access historical forecasts,stored in, for example, inventory data 116 e of database 116. Forexample, supply chain planner 110 analyzes the historical forecasts andhow they evolved from, for example, eight weeks out to one week out,which provides information necessary to identify potential demandplanning issues.

To further explain the operation of identifying potential demandplanning issues, an example is now given. In the following example,TABLE 2 provides a one week out forecast for a week-x implies theforecast generated for week-x one week before week-x, two week outforecast for week-x implies the forecast generated for week-x two weeksbefore week-x and so on.

TABLE 2 Forecast Product Jun. 13, Site_ID Week name 2008 Jun. 20, 2008Jun. 27, 2008 Jul. 4, 2008 Jul. 11, 2008 Jul. 18, 2008 Jul. 25, 2008India Jun. 07, 2008 Part-xxx 339 339 339 339 372 372 372 India Jun. 14,2008 Part-xxx 923 923 500 923 823 823 India Jun. 21, 2008 Part-xxx 9251114 909 949 1031 India Jun. 28, 2008 Part-xxx 901 901 901 901 IndiaJul. 05, 2008 Part-xxx 901 901 901 India Jul. 12, 2008 Part-xxx 889 888India Jul. 19, 2008 Part-xxx 1042

As shown in TABLE 2 for the week-ending Jul. 4, 2008, 4 week outforecast (forecast week=Jun. 6, 2008) is 339 items, 3 week out forecast(forecast week=Jun. 14, 2008) is 500 items, 2 week out forecast(forecast week=Jun. 21, 2008) is 1114 items and 1 week out forecast(forecast week=Jun. 28, 2008) is 901 items. In this example, if asupplier of one or more supply chain entities 120 a-120 n has a leadtime of 4 weeks, that is, it takes 4 weeks to receive the material oncethe order is given to the supplier due to manufacturing and/ortransportation, then for meeting the demand for Jul. 4, 2008, thesupplier would have made the shipment based on the 4 week out forecastof Jul. 4, 2008, the forecast week of Jun. 7, 2008 (i.e., 339 items).

Continuing with this example, the data in TABLE 2 shows that a user hasvaried the forecast for Jul. 4, 2008 in subsequent forecasting cyclesand has moved it upwards. However the replenishment for Jul. 4, 2008 isbe based on 339 items. So on Jul. 4, 2008; suppose the actual demand is850 items, there is a stock-out or a backlog.

In one embodiment, the root cause of this issue is shown by thehistorical forecasts and the actual demands. For example, suppose, asconfirmed from data, that replenishments have happened as planned, butthe forecast moved upwards later, meaning that 4 week-out forecastaccuracy was not good. In such cases, the root cause may be forecastingissues which provide for supply chain planner 110 to analyze whyforecast accuracy was inaccurate and how the forecast accuracy can beimproved in the future.

At step 306, supply chain planner 110 analyzes trends in summarystatistics determined at step 202 of FIG. 2 that quantify the deviationof actual demand against forecasted demand. In particular, supply chainplanner 110 determines if trends show the process to be out of controlfrom expected norms, such as for example, using techniques like WesternElectric Rules1 and Six Sigma analysis. Although particular techniquesare described, embodiments contemplate using any appropriate techniqueto perform demand error review such as, for example, Weighted MeanAbsolute Percent Error trends, Bias trends as measured by a statistical“Tracking Signal” and De-causalled (or smoothened) demand signal, orother like techniques. In addition or as an alternative, theDe-causalled demand signal, discussed below in more detail, maycompensate for lumpiness. That is, supply chain planner 110 detects whenan unexpected order, for example, an unexpected large order occurs. Inaddition, the tracking signal detects bias trends, that is, bias is, forexample, if one or more supply chain entities 120 a-120 n alwaysforecasts to high, or if one or more supply chain entities 120 a-120 nalways forecasts to low.

At step 308, supply chain planner 110 reviews actual replenishmentpatterns against forecasted demand and actual demand. For example,supply chain planner 110 identifies anomalies to isolate cases wheresupply may be out of synch with requested forecasts and/or actualdemands. In addition, or as an alternative, supply chain planner 110determines what is planned to arrive at each stock keeping location ofsupply chain entities 120 a-120 n, what is planned to be consumed, whatis the status of any stock out in the future. In addition, supply chainplanner continues to monitor the past to determine what came in and whatwent out, and synchronize.

At step 310, supply chain planner 110 reviews current planned shipmentsagainst most up-to-date need. That is, supply chain planner 110 analyzesthe forward looking, i.e., what is scheduled to come in and how is itscheduled to be consumed and whether there are any potential, forexample, code 1 or code 2 stock outs in the future, or is there too muchcoming in and shipments should be stopped. In addition, or as analternative, supply chain planner 110 evaluates proactive adjustments tosupply chain plans in collaboration with supply chain partners such as,one or more other supply chain entities 120 a-120 n. In addition, supplychain planner 110 identifies imminent stock-outs or excess inventorybuild-ups if existing plans are not altered. In another embodiment,supply chain planner 110 performs automated analysis to suggest changesto the supply chain plan that will mitigate these problems.

In one embodiment, a user associated with system 100 is provided anearly warning on what will happen in-between planning cycles. Forexample, given what the supply chain planning engine is planning to do,after the planning engine has optimized the supply chain plan, there arestill imminent stock outs that the user can still do something about,based on the early warnings in-between planning cycles. In addition,embodiments provide for the ability of the user to take additionalliberties that may not have been accounted for in the models.

At step 312, supply chain planner 110 reviews the replenishment patternsfrom step 308. In this step, supply chain planner 110 reviews inventorypolicy parameters 116 d such as minimum safety stock targets,replenishment intervals, sourcing splits and reorder quantities todetermine any significant changes from previously published parameters.In one embodiment, supply chain planner 110 focuses on understandingwhat changed and why. Inventory policy parameters 116 d will only changeif, for example, input data changes. As an example only, and not by wayof limitations, supply chain planner 110 communicates that the demandhas changed to a user of system 100 and exposes an inside about thesupply chain network. In addition, supply chain planner 110 isolatesroot causes by pin-pointing specific input data changes that result ininventory policy parameters 116 d changes.

At step 314, supply chain planner 110 identifies alert rules pinpointing“likely suspects” via automated rules based inferences (i.e. businessrules 116 c). In addition, supply chain planner 110 confirms root causevia collaboration and consensus between supply chain functions such asdemand planning, supply planning, buyers, suppliers and the like.

To further explain the identification of root causes, examples are nowgiven. In the following examples, supply chain planner 110 accessesdatabase 116 to determine root causes. For example, if supply chainplanner 110 determines that the historical forecast demand isconsistently higher than actual demand, which may, for example, lead toexcess inventory, and replenishments are in synch with forecasts, thenthe root cause is to correct the forecast, such that it is in synch withactual demand.

In addition, if a supplier of one or more supply chain entities 120a-120 n has a replenishment pattern, wherein the replenishment patternis, for example, every alternate week, but, the supplier madereplenishments irrespective of the existing inventory, because of, forexample, a replenishment policy. Then, the supplier is not replenishingto maintain the actual inventory equal to optimal inventory which leadsto a high inventory. Furthermore, if supply chain planner 110 determinesthat the historical forecast given for replenishment is in synch withactual demands, but that the replenishments are not in synch with theforecast demands, than backlogs or excess inventory can occur.

If supply chain planner 110 determines that the historical forecast isin synch with actual demands for maximum weeks, when the actual demandshas increased. As a result, the inventory falls drastically, since thereplenishments were based on historical forecasts and hence theinventory levels continued to be low. If supply chain planner 110determines that the replenishments are not regular and as a result, theinventory keeps swinging from being excess to shortages. If supply chainplanner 110 determines that the item was consumed for the last timeafter there are no longer any shipments or consumptions. Than theinventory is aging inventory, after the item has reached its end of lifeand the aging inventory needs to be cleared up.

As discussed above, supply chain planner 110 determines root causeswhich are then analyzed in a parato analysis, to stack up the rootcauses to determine which root causes have the most impact on variousbusiness rules 116 c, such as for example, costs, business, and thelike. In one embodiment, supply chain planner 110 addresses theidentified root causes and the affects on the outcome of policiesparameters 116 d or the supply chain network to minimize future likeoccurrences.

At step 316, supply chain planner 110 prioritizes the root causes inorder of business impact, importance, urgency and effort needed forshort and long-term resolution according to business rules 116 c. Atstep 318, supply chain planner 110 provides proactive and reactiveactions (both short term and/or long-term) to not only avoid imminentstock-outs or excesses but also to mitigate or eliminate root causesfrom occurring in the future. For example, some of the typical actionsthat supply chain planner 110 provides is adjustments to demandforecasts, adjustments to Material Requirements Plans (MRP) published tosuppliers, changes to shipment plans, adjustments to published inventorypolicy parameters (such as safety stock targets), actions to re-assignor dispose aging inventory, and corrections to data in source systems.

In one embodiment, supply chain planner 110 feeds step 318 actions intothe next planning cycle (i.e., step 202), which results in a system andprocess that are self-tuning with each decision-action loop turning intoa cycle of learning. For example, as one or more supply chain entities120 a-120 n utilize the framework of the present invention, these one ormore supply chain entities 120 a-120 n are able to transform to alearning organization that is constantly improving and tuning theinventory policy parameters in response to changing market conditions.

FIG. 4 illustrates an exemplary method 300 of analyzing the deviation ofactual demand against forecasted demand and providing alert rules. Asdiscussed above, supply chain planner 110 analyzes the deviation ofactual demand against forecasted demand and triggers one or more alertrules according to business rules 116 c of database 116. Supply chainplanner 110 determines at step 402 if the Weighted Mean Absolute PercentError for an item-location for the most recent time-period exceeds anoutlier threshold (that is, it was “capped”), if so, supply chainplanner 110 triggers a forecast error outlier rule alert. At step 404,supply chain planner 110 determines if the Weighted Mean AbsolutePercent Error for an item-location for the most recent time-periodexceeds a “high-error” threshold but does not exceed the outlierthreshold, if so, supply chain planner 110 triggers a high forecasterror rule alert.

At step 406, supply chain planner 110 determines if theitem-location-time-period is coded as an inventory stocked out ordangerously below target and Actual>L*Decausaled Actuals, supply chainplanner 110 flags a large order rule alert for thisitem-location-time-period, wherein L is a configurable parameter with adefault value of, for example, 1.3. In addition, supply chain planner110 computes the Decausaled Actuals quantity using, for example, acentered moving average, as discussed below in more detail.

To further explain the operation of computing Decausaled Actuals anexample is now given. In the following example a 5 week moving averageis provided below in TABLE 3.

TABLE 3 “Decausaled Rows Actuals” = 5 % deviation considered Week ofActual in centered Centered from Item Week Actual moving Movingsmoothened Row Site_ID Number Ending Shipments average Average cons 1Site_A Item_X Jun. 6, 2008 704 2 Site_A Item_X Jun. 13, 2008 704 3Site_A Item_X Jun. 20, 2008 704 1, 2, 3, 4, 5 792 −11% 4 Site_A Item_XJun. 27, 2008 880 2, 3, 4, 5, 6 1003 −12% 5 Site_A Item_X Jul. 4, 2008968 3, 4, 5, 6, 7 1056 −8% 6 Site_A Item_X Jul. 11, 2008 1760 4, 5, 6,7, 8 1390 27% 7 Site_A Item_X Jul. 18, 2008 968 5, 6, 7, 8, 9 1619 −40%8 Site_A Item_X Jul. 25, 2008 2376 6, 7, 8, 9 1782 33% 9 Site_A Item_XAug. 1, 2008 2024 7, 8, 9 1789 13% 10 Site_A Item_X Aug. 8, 2008

In TABLE 3 it is assumed that the “current week” is week ending Aug. 8,2008 and “today” is between Aug. 2, 2008 and Aug. 8, 2008. Which is why,for example, that there is no data for the Actual Shipments column forthe week of Aug. 8, 2008. Accordingly, the 5-week Decauseled movingaverage for the most recent two weeks ending Jul. 25, 2008 and Aug. 1,2008 are based on 4 weeks and 3 weeks of data respectively.

At step 408, supply chain planner 110 determines if the trackingsignal>P, if so, supply chain planner 110 flags a positive bias alertrule for each week this condition is met, wherein the threshold P is aconfigurable parameter with a default value of, for example 6. At step410, supply chain planner 110 determines if the tracking signal<N, ifso, supply chain planner 110 flags a negative bias alert rule for eachweek this condition is met, wherein the threshold N is a configurableparameter with a default value of for example-6.

At step 412, supply chain planner 110 determines if an item has beencoded as inventory stocked out or dangerously below target for R or moreweeks, consumption has been 0 for Z or more weeks and there was no largeorders in the last 4 weeks, if so, supply chain planner 110 triggers apossible end-of-life alert rule. R and Z are configurable parameterswith default values of, for example, R=3, Z=3. At step 414, supply chainplanner 110 determines if an item has been coded as inventory stockedout or dangerously below target or as inventory too low and at high riskof stock-out for the last 4 weeks, and the item does not meet aPossible-End-of-Life alert rule, a positive bias alert condition, or alarge order alert condition, if so, supply chain planner 110 triggers alow replenishment alert rule.

At step 416, supply chain planner 110 determines if the item has beencoded as inventory to high or inventory is excessively too high andlikely at risk of obsolescent for the past 4 weeks and there is nopositive bias and there is no negative bias, if so, supply chain planner110 triggers an aging inventory alert rule.

In one embodiment, supply chain planner 110 provides a sequence to eachof the alert rules. In addition, or as an alternative, if the sameitem-location-time-period qualifies for more than one alert rule, thensupply chain planner 110 determines which one will have priority, suchas, the last one is given priority. In addition, supply chain planner110 provides the following sequence of alert rules: high forecast error,forecast error outlier, large order, positive bias, negative bias,possible end-of-life, low replenishment, and aging inventory. Although,particular alert rules and a particular sequence is described,embodiments contemplate any particular alert rule and/or sequencewithout departing from the scope of the present invention.

Reference in the foregoing specification to “one embodiment”, “anembodiment”, or “another embodiment” means that a particular feature,structure, or characteristic described in connection with the embodimentis included in at least one embodiment of the invention. The appearancesof the phrase “in one embodiment” in various places in the specificationare not necessarily all referring to the same embodiment.

While the exemplary embodiments have been shown and described, it willbe understood that various changes and modifications to the foregoingembodiments may become apparent to those skilled in the art withoutdeparting from the spirit and scope of the present invention.

1. A system for root cause analysis and early warning of inventoryproblems, comprising: a database configured to store: data describinginventory policy parameters of a supply chain network; data describingone or more demand patterns and one or more replenishment patterns ofthe supply chain network; and data describing the supply chain networkcomprising a plurality of entities, each entity configured to supply oneor more items to satisfy a demand; and a server coupled with thedatabase and configured to: access the data describing the inventorypolicy parameters; access the data describing the one or more demandpatterns and the data describing the one or more replenishment patterns;access the data describing the supply chain network; optimize theinventory policy parameters for each of the one or more items accordingto the one or more demand patterns and the one or more replenishmentpatterns; and store the optimized inventory policy parameters in thedatabase for each of the one or more items.
 2. The system of claim 1,wherein the one or more demand patterns comprises data representinghistorical forecasted demand and actual demand for the one or moreitems.
 3. The system of claim 1, wherein the one or more replenishmentpatterns comprises data representing the size and frequency ofreplenishments for the one or more items.
 4. The system of claim 1,wherein the server is further configured to generate an inventoryreplenishment plan in order to meet demand and to maintain inventorylevels within one or more of the inventory policy parameters.
 5. Thesystem of claim 1, wherein the server is further configured to assigncoding values to each of the one or more items according to inventorylevels of each of the plurality of entities.
 6. The system of claim 1,wherein the server is further configured to perform a demand errorreview to quantify the deviation of actual demand against forecasteddemand.
 7. The system of claim 1, wherein the server is furtherconfigured to identify one or more root causes and generate one or morealert rules to modify one or more of the inventory policy parameters. 8.A computer-implemented method of root cause analysis and early warningof inventory problems, comprising: accessing, by a computer, datadescribing inventory policy parameters; accessing, by the computer, datadescribing one or more demand patterns and data describing one or morereplenishment patterns; accessing, by the computer, data describingsupply chain network; optimizing, by the computer, the inventory policyparameters for each of one or more items according to the one or moredemand patterns and the one or more replenishment patterns; and storing,by the computer, the optimized inventory policy parameters in a databasefor each of the one or more items.
 9. The method of claim 8, wherein theone or more demand patterns comprises data representing historicalforecasted demand and actual demand for the one or more items.
 10. Themethod of claim 8, wherein the one or more replenishment patternscomprises data representing the size and frequency of replenishments forthe one or more items.
 11. The method of claim 8, further comprisinggenerating an inventory replenishment plan in order to meet demand andto maintain inventory levels within one or more of the inventory policyparameters.
 12. The method of claim 8, further comprising assigningcoding values to each of the one or more items according to inventorylevels of each of the plurality of entities.
 13. The method of claim 8,further comprising performing a demand error review to quantify thedeviation of actual demand against forecasted demand.
 14. The method ofclaim 8, further comprising identifying one or more root causes andgenerating one or more alert rules to modify one or more of theinventory policy parameters.
 15. A computer-readable medium embodiedwith software enabling root cause analysis and early warning ofinventory problems in a supply chain network, the software when executedusing one or more computers is configured to: access data describinginventory policy parameters; access data describing one or more demandpatterns and data describing one or more replenishment patterns; accessdata describing the supply chain network; optimize the inventory policyparameters for each of one or more items according to the one or moredemand patterns and the one or more replenishment patterns; and storethe optimized inventory policy parameters in a database for each of theone or more items.
 16. The computer-readable medium of claim 15, whereinthe one or more demand patterns comprises data representing historicalforecasted demand and actual demand for the one or more items.
 17. Thecomputer-readable medium of claim 15, wherein the one or morereplenishment patterns comprises data representing the size andfrequency of replenishments for the one or more items.
 18. Thecomputer-readable medium of claim 15, wherein the software is furtherconfigured to generate an inventory replenishment plan in order to meetdemand and to maintain inventory levels within one or more of theinventory policy parameters.
 19. The computer-readable medium of claim15, wherein the software is further configured to assign coding valuesto each of the one or more items according to inventory levels of eachof the plurality of entities.
 20. The computer-readable medium of claim15, wherein the software is further configured to perform a demand errorreview to quantify the deviation of actual demand against forecasteddemand.
 21. The computer-readable medium of claim 15, wherein thesoftware is further configured to identify one or more root causes andgenerate one or more alert rules to modify one or more of the inventorypolicy parameters.